Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications

  • Stefan KlampflEmail author
  • Roman Kern
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 548)


Scholarly publishing increasingly requires automated systems that semantically enrich documents in order to support management and quality assessment of scientific output. However, contextual information, such as the authors’ affiliations, references, and funding agencies, is typically hidden within PDF files. To access this information we have developed a processing pipeline that analyses the structure of a PDF document incorporating a diverse set of machine learning techniques. First, unsupervised learning is used to extract contiguous text blocks from the raw character stream as the basic logical units of the article. Next, supervised learning is employed to classify blocks into different meta-data categories, including authors and affiliations. Then, a set of heuristics are applied to detect the reference section at the end of the paper and segment it into individual reference strings. Sequence classification is then utilised to categorise the tokens of individual references to obtain information such as the journal and the year of the reference. Finally, we make use of named entity recognition techniques to extract references to research grants, funding agencies, and EU projects. Our system is modular in nature. Some parts rely on models learnt on training data, and the overall performance scales with the quality of these data sets.


PDF extraction Machine learning Named entity recognition 



The presented work was in part developed within the CODE project (grant no. 296150) and within the EEXCESS project (grant no. 600601) funded by the EU FP7, as well as the TEAM IAPP project (grant no. 251514) within the FP7 People Programme. The Know-Center is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Know-Center GmbHGrazAustria

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